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Call for Papers
Special issue on Recommenders on the Web
ACM Transactions on the Web
GUEST EDITORS
John Riedl
Barry Smyth
Recommender systems are changing the way people interact with the
Web. From e-commerce sites like Amazon.com to news and information
sites like digg and slashdot, recommenders help people choose between
diverse products and complex information, by providing a more
personalized information access experience. While much of the
published research on recommenders has focused on the algorithms that
power the recommendation process, many research challenges remain,
especially when it comes to the applications, interfaces and social
implications of recommenders.
This special issue of ACM Transactions on the Web aims to gather a
collection of high quality contributions that reflect recent
innovations in the field of Web-based recommender systems. Papers may
focus on novel Web interfaces for recommenders, on emerging
applications of recommenders, or on the ways recommenders fit into the
Social Web. Particular areas of interest include, but are not limited
to:
Applications of Recommenders on the Web
Recommenders on the Web, including the mobile Web and e-commerce;
recommenders and community, including group recommenders, recommenders
to support social networking, and techniques for leveraging the social
graph in forming recommendations.
Recommendation Interfaces
Novel recommendation interfaces, including Web, mobile, and Web2.0;
emerging interface technologies, including haptic interfaces, group
interfaces, and public-displays; the role of explanations in
recommender systems; evaluating recommendation interfaces through user
studies and other HCI approaches.
The Social Implications of Recommender Systems
User privacy in recommender systems; privacy-preserving recommendation
techniques; security and data protection; the robustness of
recommender systems (e.g., to recommendation spam); techniques for
detecting and coping with malicious users; on the role of trust in
recommender systems; computational models of trust for recommender
applications.
Recommender Algorithms for the Web
Novel algorithms especially suited to Web applications; evaluation
techniques for algorithms that effectively predict performance in
practice; hybrid collaborative and content-based recommenders;
conversational recommenders.
Guest Editor Contact Information
Professor John Riedl
Department of Computer Science and Engineering
University of Minnesota
Minneapolis, MN 55455
[log in to unmask]
http://www.cs.umn.edu/~riedl
Professor Barry Smyth
Digital Chair of Computer Science,
School of Computer Science and Informatics,
College of Engineering Mathematical and Physical Sciences,
University College Dublin, Belfield, Dublin 4, Ireland.
Barry Smyth <[log in to unmask]>
Tel: +353-1-7162473 | Fax: +353-1-2697262
http://csiweb.ucd.ie/Staff/AcademicStaff/bsmyth/
Submission Information
Prospective authors, please submit your paper according to the
directions on the ACM TWEB Web site following the content and
formatting guidelines available at
http://www.acm.org/tweb/author.html. There you can also find detailed
information about the ACM TWEB review process. When submitting your
paper, please mention that it is to be considered for the special
issue on Recommenders on the Web. In addition, please send a copy of
your paper to <[log in to unmask]> and <[log in to unmask]>, with the
Subject line "TWeb: Recommenders on the Web".
Papers due: September 15, 2008
Author notification: January 30, 2009
Revised versions of accepted papers due: March 16, 2009
(all accepted papers expected to undergo a minor set of
revisions)
Final materials for publication due: May 1, 2009
Special issue published: August 2009 (tentative)
--
Mark Sanderson
Reader in Information Retrieval
Room 225, Dept. of Information Studies
University of Sheffield, Regent Court
Portobello St, Sheffield, S1 4DP, UK
Tel: +44 (0) 114 22 22648, Fax: +44 (0) 114 27 80300
mailto:[log in to unmask], http://dis.shef.ac.uk/mark
Good judgement comes from experience, experience comes from bad judgement
